Project Summary/Abstract: Processing acoustic communication signals is among the most difficult yet vital abilities of the auditory system. These abilities lie at the heart of language and speech processing, and their success or failure has profound impacts on quality of life across the lifespan. Understanding the neurobiological mechanisms that support these basic abilities holds promise for advancing assistive listening devices, and for improving diagnoses and treatments for learning disabilities and communication disorders, such as auditory processing disorder, dyslexia, and specific language impairment. Non-invasive neuroscience techniques in humans reveal the loci of language-related processing but do not answer how individual neurons and neural circuits implement language-relevant computations. Thus, circuit-level neuro-computational mechanisms that support acoustic communication signal processing remain poorly understood. Multiple lines of research suggest that songbirds can provide an excellent model for investigating shared auditory processing abilities relevant to language. This proposal investigates neural mechanisms of auditory temporal pattern processing abilities shared between songbirds and humans. In Aim 1, we test the cellular-level predictions of a powerful modelling framework, called predictive coding, proposed as a general computational mechanism to support the learned recognition of complex temporally patterned signals at multiple timescales. We combine state-of-the-art machine learning methods with multi-electrode electrophysiology, to test explicit models for natural stimulus representation, prediction, and error coding in single cortical neurons and neural populations. One aspect of auditory perception integral to speech is the discretization of the signal into learned categorically perceived sounds (phonemes). In Aim 2, we use the predictive coding framework to investigate the learned categorical perception of natural auditory categories in populations of cortical neurons. In humans, the transition statistics between adjacent phonemes can aid or alter phoneme categorization, providing cues for language learners and listeners to disambiguate perceptually similar sounds. Aim2 also examines how categorical neural representations are affected by temporal context. In addition to which phonemes occur in a sequence, speech processing also requires knowing where those elements occur. Sensitivities to the statistical regularities of speech sequences are established long before infants learn to speak, and continue to affect both recognition and comprehension throughout adulthood. Songbirds also attend to the statistical regularities in their vocal communication signals. In Aim 3, we focus on how sequence-specific information is encoded by single neurons and neural populations in auditory cortex. The proposed approach permits progress in the near term towards establishing the basic neurobiological substrates of foundational language-relevant abilities and a general framework within which more complex, uniquely human processes, can be proposed and eventually tested.